Following the setting of [1]}, [2]}, we use the sliding window fashion to model region similarity between groundtruth and saliency map. The corresponding regions are denoted as \(M_i^{(n)} = \lbrace M_i^{(n)}:i=1,...D\rbrace \)  and \(T_i^{(n)} = \lbrace T_i^{(n)}:i=1,...D\rbrace \) , where \(D\)  is the total number of region. Then we use SSIM to evaluate the similarity between \(M_i^{(n)}\)  and \(G_i^{(n)}\) , which is defined as:
\(SSD_i^{(n)} = \frac{ (2\mu _m\mu _t+C_1)(2\sigma _{mt}+C_2) }{ (\mu _m^2+\mu _t^2+C_1)(\sigma _m^2+\sigma _t^2+C_2)}\) 
